A Scalable and Accurate Chessboard-Based AMC Algorithm With Low Computing Demands
Automatic Modulation Classification (AMC) is a technique used to identify signal modulations in applications like IoT devices, cognitive radar, software-defined radio, and electronic warfare. These applications could be applied to IoT devices. With future wide applications of IoT devices, AMC algori...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10298211/ |
| _version_ | 1797635419285749760 |
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| author | Yuqin Zhao William C. J. Gavin Tiantai Deng Edward A. Ball Luke Seed |
| author_facet | Yuqin Zhao William C. J. Gavin Tiantai Deng Edward A. Ball Luke Seed |
| author_sort | Yuqin Zhao |
| collection | DOAJ |
| description | Automatic Modulation Classification (AMC) is a technique used to identify signal modulations in applications like IoT devices, cognitive radar, software-defined radio, and electronic warfare. These applications could be applied to IoT devices. With future wide applications of IoT devices, AMC algorithms need to be more compact yet suitable for embedded devices with limited resources and remain acceptable accuracy. Although current AMC algorithms deliver high accuracy, they require substantial computing power, making them unsuitable for IoT devices. This paper introduces the novel Chessboard-based Automatic Modulation Classification (CAMC) algorithm, which has dramatically high accuracy. Test results reveal that CAMC achieves 99%* accuracy under a 3dB SNR condition and 100% above 5dB SNR. Meanwhile, this algorithm is scalable and demands less computing power. It offers better accuracy results compared to state-of-the-art AMC algorithms, classifying mainstream modulations in IoT devices like BPSK, QPSK, 8PSK, and 16QAM, but requires less computing power than existing algorithms. Additionally, CAMC is hardware-friendly due to its inherent parallelism and scalability. The novelty of this paper is to classify 4 different modulations in a low-computation-loading required and hardware-friendly way and achieve a high accuracy of over 99%* above SNR of 3dB. (* Accuracy that most of the time could reach) |
| first_indexed | 2024-03-11T12:21:49Z |
| format | Article |
| id | doaj.art-85c3a010b5a6471ea947f906cbf6df1e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T12:21:49Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-85c3a010b5a6471ea947f906cbf6df1e2023-11-07T00:01:06ZengIEEEIEEE Access2169-35362023-01-011112095512096210.1109/ACCESS.2023.332820510298211A Scalable and Accurate Chessboard-Based AMC Algorithm With Low Computing DemandsYuqin Zhao0https://orcid.org/0000-0001-7943-1433William C. J. Gavin1Tiantai Deng2https://orcid.org/0000-0003-4507-5746Edward A. Ball3https://orcid.org/0000-0002-6283-5949Luke Seed4https://orcid.org/0000-0003-4648-5616Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, U.K.Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, U.K.Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, U.K.Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, U.K.Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, U.K.Automatic Modulation Classification (AMC) is a technique used to identify signal modulations in applications like IoT devices, cognitive radar, software-defined radio, and electronic warfare. These applications could be applied to IoT devices. With future wide applications of IoT devices, AMC algorithms need to be more compact yet suitable for embedded devices with limited resources and remain acceptable accuracy. Although current AMC algorithms deliver high accuracy, they require substantial computing power, making them unsuitable for IoT devices. This paper introduces the novel Chessboard-based Automatic Modulation Classification (CAMC) algorithm, which has dramatically high accuracy. Test results reveal that CAMC achieves 99%* accuracy under a 3dB SNR condition and 100% above 5dB SNR. Meanwhile, this algorithm is scalable and demands less computing power. It offers better accuracy results compared to state-of-the-art AMC algorithms, classifying mainstream modulations in IoT devices like BPSK, QPSK, 8PSK, and 16QAM, but requires less computing power than existing algorithms. Additionally, CAMC is hardware-friendly due to its inherent parallelism and scalability. The novelty of this paper is to classify 4 different modulations in a low-computation-loading required and hardware-friendly way and achieve a high accuracy of over 99%* above SNR of 3dB. (* Accuracy that most of the time could reach)https://ieeexplore.ieee.org/document/10298211/Communications technologyclassification algorithmsmodulationInternet of Thingsparallel algorithmsimage classification |
| spellingShingle | Yuqin Zhao William C. J. Gavin Tiantai Deng Edward A. Ball Luke Seed A Scalable and Accurate Chessboard-Based AMC Algorithm With Low Computing Demands IEEE Access Communications technology classification algorithms modulation Internet of Things parallel algorithms image classification |
| title | A Scalable and Accurate Chessboard-Based AMC Algorithm With Low Computing Demands |
| title_full | A Scalable and Accurate Chessboard-Based AMC Algorithm With Low Computing Demands |
| title_fullStr | A Scalable and Accurate Chessboard-Based AMC Algorithm With Low Computing Demands |
| title_full_unstemmed | A Scalable and Accurate Chessboard-Based AMC Algorithm With Low Computing Demands |
| title_short | A Scalable and Accurate Chessboard-Based AMC Algorithm With Low Computing Demands |
| title_sort | scalable and accurate chessboard based amc algorithm with low computing demands |
| topic | Communications technology classification algorithms modulation Internet of Things parallel algorithms image classification |
| url | https://ieeexplore.ieee.org/document/10298211/ |
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